Training apparatus and method

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395 21, 395 23, G06E 100, G06E 300, G06F 1518

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active

056713339

ABSTRACT:
Apparatus and methods for training classifiers. The apparatus includes a degree of certainty classifier which classifies examples in categories and indicates a degree of certainty regarding the classification and an annotating classifier which receives classified examples with a low degree of certainty from the degree of certainty classifier and annotates them to indicate whether their classifications are correct. The annotated examples are then used to train another classifier. In one version of the invention, the other classifier is a new version of the degree of certainty classifier, and training continues until the degree of certainty classifier has satisfactory performance. The degree of certainty classifier of the embodiment is a probabilistic binary classifier which is trained using relevance feedback. The annotating classifier may include an interactive interface which permits a human user of the system to examine an example and indicate whether it was properly classified.

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D.J.C. MacKay, "The Evidence Framework Applied to Classification Networks", Neural Computation, vol. 4, pp. 720-736 (1992).
J-N. Hwang, J.J. Choi, S. Oh, R. Marks, II, "Query-Based Learning Applied to Partially Trained Multilayer Perceptrons", IEEE Transactions on Neural Networks, vol. 2, No. 1, pp. 131-136, Jan. 1991.
L. Atlas, D. Cohn, R. Ladner, "Training Connectionist Networks with Queries and Selective Sampling", Advances in Neural Information Processing Systems 2, Ed. D.S. Touretzky, pp. 566-573, 1990.
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W.A. Gale, K.W. Church, D. Yarowsky, "A Method for Disambiguating Word Series in a Large Corpus", Computers and the Humanities, vol. 26, pp. 415-439, 1993.

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